Clustering of data sets with missing values using statistical imputation methods / (Record no. 532)

MARC details
000 -LEADER
fixed length control field 02214nam a2200241 4500
001 - CONTROL NUMBER
control field UPMIN-00000010897
003 - CONTROL NUMBER IDENTIFIER
control field UPMIN
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20230201165228.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 230201b |||||||| |||| 00| 0 eng d
040 ## - CATALOGING SOURCE
Original cataloging agency DLC
Transcribing agency UPMin
Modifying agency upmin
041 ## - LANGUAGE CODE
Language code of text/sound track or separate title eng
090 ## - LOCALLY ASSIGNED LC-TYPE CALL NUMBER (OCLC); LOCAL CALL NUMBER (RLIN)
Classification number (OCLC) (R) ; Classification number, CALL (RLIN) (NR) LG993.5 2000
Local cutter number (OCLC) ; Book number/undivided call number, CALL (RLIN) A64 M34
100 1# - MAIN ENTRY--PERSONAL NAME
Personal name Macabenta, Mel Zha Leah M.
9 (RLIN) 2020
245 00 - TITLE STATEMENT
Title Clustering of data sets with missing values using statistical imputation methods /
Statement of responsibility, etc. Mel Zha Leah M.Macabenta.
260 ## - PUBLICATION, DISTRIBUTION, ETC.
Date of publication, distribution, etc. 2000
300 ## - PHYSICAL DESCRIPTION
Extent 65 leaves.
502 ## - DISSERTATION NOTE
Dissertation note Thesis (BS Applied Mathematics) -- University of the Philippines Mindanao, 2000
520 3# - SUMMARY, ETC.
Summary, etc. K-means clustering algorithm is the most widely used clustering algorithm in the field of data analysis. One major drawback of this algorithm is that it can never accommodate data set with missing values. However, in reality, occurrence of missing values can not be avoided. Imputation methods are more extensively used in treating missing values compared to deletion. Several imputation methods are suggested but each has advantages and disadvantages over the others, so proper choice of imputation methods is very necessary. Two of the statistical imputation methods namely, hot deck imputation and imputation using a prediction model were used in treating the incomplete data sets. The incomplete data sets after treatment were then clustered using the K-means clustering algorithm. To have a clear comparison, five data sets were used with two kinds of missingness, missing completely at random (MCAR) and missing at random (MAR) at five different levels of degradation ranging from 1% missing values. The evaluation of the resulting clusters was done using the adjusted Rand index. The two methods were compared to the modified K-means algorithm, particularly the modified Euclidean distance. Results showed that the hot deck imputation, regression method and modified K-means clustering algorithm attained a high recovery of clusters especially with big data sets until 30% levels of missing values. In small data sets, good recovery is attained until 10% level of missing values only.
658 ## - INDEX TERM--CURRICULUM OBJECTIVE
Main curriculum objective Undergraduate Thesis
Curriculum code AMAT200
905 ## - LOCAL DATA ELEMENT E, LDE (RLIN)
a Fi
905 ## - LOCAL DATA ELEMENT E, LDE (RLIN)
a UP
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme Library of Congress Classification
Koha item type Thesis
Holdings
Withdrawn status Lost status Source of classification or shelving scheme Damaged status Status Collection Home library Current library Shelving location Date acquired Source of acquisition Accession Number Total Checkouts Full call number Barcode Date last seen Price effective from
    Library of Congress Classification   Not For Loan Preservation Copy University Library University Library Archives and Records 2006-06-27 donation UAR-T-gd745   LG993.5 2000 A64 M34 3UPML00021978 2022-09-21 2022-09-21
    Library of Congress Classification   Not For Loan Room-Use Only College of Science and Mathematics University Library Theses 2006-06-27 donation CSM-T-gd1430   LG993.5 2006 A64 M34 3UPML00011616 2022-09-21 2022-09-21
 
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